United Republic of Tanzania environment data

**ADAM ID: FL-20231117-TZA-00** Flood covering 238073.0 sq m on Nov 17 2023 in Tanzania, United Republic of. It impacted 201641 people.

This dataset is the output from the Global Flood Model intercomparison project undertaken on behalf of the Global Flood Partnership. The methodology is described in Trigg et al., 2016, "The credibility challenge for global fluvial flood risk analysis," published in the Environmental Research Letters Journal. Each file is a geospatial, WGS84, 1/1200 decimal degrees resolution (~90m) GeoTIFF raster with a classified integer value representing how many out of the 6 models agree that a cell is wet (note 0 - dry/no data)

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

This soil organic carbon dataset contains the following maps: soil organic carbon concentration (%) for the 0-10 cm, 10-20 cm, 20-30 cm and 0-30 cm soil layers, and bulk density (kg/m3) and soil organic carbon stock (kg/m2) for the 0-30 cm layer. These maps were produced with (geostatistical) regression-kriging models that combined soil data from the NAFORMA survey, the Tanzania National Soil Survey and the African Soil Profiles Database Version 1.1 with a suite of environmental GIS data layers including a land cover map, SOTER soil class map, maps of topographic attributes derived from the SRTM-DEM, maps of surface reflectance and vegetation indices derived from satellite imagery. The regression-kriging models were used to predict carbon concentrations, stocks and bulk density at the nodes of a regular grid with 250 meter cell size covering the Tanzania. Prediction uncertainty was quantified and is available with the data as the lower and upper boundary of the 90% prediction interval. Further details about the input data, modelling framework, and cross validation results are provided in a peer-reviewed, scientific journal article. The project was funded by the UN-REDD Programme Output 2.4 “National Maps inform delivery of the REDD+ Framework” and conducted through a letter of Agreement between Food and Agricultural Organization of the United Nations (FAO) and ISRIC-World Soil Information. The maps were produced by ISRIC - World Soil Information in a collaborative effort with the National Soil Survey, Ministry of Natural Resources and Tourism, Tanzania Forest Services, Sokoine University, and the AfSIS project.